Learning model generating device, type identification system, and learning model generation method for generating learning model and using generated learning model to infer type of image defect

ABSTRACT

A learning model generating device includes a first image reading device and a first control device. The first control device includes a processor and functions, through the processor executing a first control program, as a first segmenter, a learning model generator, and a first compressor. The first segmenter segments each of images of training prints obtained by reading performed by the first image reading device. The learning model generator learns segmented images to generate a first learning model for use in inferring a type of an image defect. The first compressor compresses each of the images of the training prints. The first segmenter segments each of compressed images obtained by compression performed by the first compressor. The learning model generator learns compressed and segmented images to generate a second learning model for use in inferring a type of an image defect.

INCORPORATION BY REFERENCE

This application claims priority to Japanese Patent Application No.2018-224021 filed on Nov. 29, 2018, the entire contents of which areincorporated by reference herein.

BACKGROUND

The present disclosure relates to learning model generating devices,type identification systems, and methods for generating learning modelsand particularly relates to a technique for generating a learning modeland using the generated learning model to infer the type of an imagedefect.

Recently, at production sites and the like, a mechanism is beingintroduced for automating appearance inspection and so on of productsusing artificial intelligence or deep learning.

SUMMARY

A technique improved over the above technique is proposed as one aspectof the present disclosure.

A learning model generating device according to one aspect of thepresent disclosure is a learning model generating device capable oflearning a data set containing image defect-containing training printsprepared for each type of image defect to generate a learning model andincludes a first image reading device and a first control device. Thefirst image reading device reads images of the training prints. Thefirst control device includes a processor and functions, through theprocessor executing a first control program, as a first segmenter, alearning model generator, and a first compressor. The first segmentersegments, on a predetermined first segmentation condition, each of theimages of the training prints obtained by reading performed by the firstimage reading device. The learning model generator learns segmentedimages obtained by segmentation performed by the first segmenter togenerate a first learning model for use in inferring a type of an imagedefect. The first compressor compresses, on a predetermined firstcompression condition, each of the images of the training printsobtained by reading performed by the first image reading device. Inaddition, the first segmenter segments, on a predetermined secondsegmentation condition, each of compressed images obtained bycompression performed by the first compressor. The learning modelgenerator learns compressed and segmented images obtained by compressionperformed by the first compressor and segmentation performed by thefirst segmenter to generate a second learning model for use in inferringa type of an image defect.

A type identification system according to one aspect of the presentdisclosure is a type identification system including the above-describedlearning model generating device and a type identification device. Thetype identification device uses the first learning model and the secondlearning model both generated by the learning model generating device toidentify a type of an image defect contained on a print to be inspected.The type identification device includes a second image reading deviceand a second control device. The second image reading device reads animage of the print to be inspected. The second control device includes aprocessor and functions, through the processor executing a secondcontrol program, as a second segmenter, an inferrer, and a secondcompressor. The second segmenter segments, on the same segmentationcondition as the first segmentation condition, the image of the print tobe inspected obtained by reading performed by the second image readingdevice. The inferrer uses the first learning model to infer a type of animage defect to which each of segmented images obtained by segmentationperformed by the second segmenter applies. The second compressorcompresses, on the same compression condition as the first compressioncondition, the image of the print to be inspected. In addition, thesecond segmenter segments, on the same segmentation condition as thesecond segmentation condition, a compressed image obtained bycompression performed by the second compressor. The inferrer uses thesecond learning model to infer a type of an image defect to which eachof compressed and segmented images obtained by compression performed bythe second compressor and segmentation performed by the second segmenterapplies.

A method for generating a learning model according to one aspect of thepresent disclosure is a method for generating a learning model bylearning a data set containing image defect-containing training printsprepared for each type of image defect and includes an image readingstep, a segmentation step, a learning model generating step, and acompression step. In the image reading step, images of the trainingprints are read. In the segmentation step, each of the images of thetraining prints obtained by reading in the image reading step issegmented on a predetermined first segmentation condition. In thelearning model generating step, segmented images obtained bysegmentation in the segmentation step are learned to generate a firstlearning model for use in inferring a type of an image defect. In thecompression step, each of the images of the training prints iscompressed on a predetermined first compression condition. In addition,in the segmentation step, each of compressed images obtained bycompression in the compression step is segmented on a predeterminedsecond segmentation condition. In the learning model generating step,compressed and segmented images obtained by compression in thecompression step and segmentation in the segmentation step are learnedto generate a second learning model for use in inferring a type of animage defect.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional block diagram schematically showing an essentialinternal configuration of a type identification system made up byincluding a learning model generating device and a type identificationdevice each according to an embodiment of the present disclosure.

FIGS. 2A, 2B, and 2C are diagrams each for illustrating image regions ofsegmented images.

FIG. 3 is a flowchart showing an example of a sequence of processingoperations executed by a first control device in the learning modelgenerating device.

FIG. 4 is a flowchart showing an example of a sequence of processingoperations executed by a second control device in the typeidentification device.

FIG. 5 is a diagram for illustrating a flow of generation of a learningmodel.

FIG. 6 is a diagram for illustrating a flow of inference of anappropriate output from an unknown input using the learning model.

FIG. 7A is a view showing a print on which an image defect of colorunevenness forms.

FIG. 7B is a view showing a state where the image formed on the print issegmented and showing a block of the image segmented.

DETAILED DESCRIPTION

Hereinafter, a description will be given of a learning model generatingdevice, a type identification device, a method for generating a learningmodel, and a type identification system all according to one embodimentof the present disclosure with reference to the drawings. FIG. 1 is afunctional block diagram schematically showing an essential internalconfiguration of the type identification system made up by including thelearning model generating device and the type identification device eachaccording to the one embodiment. The type identification system 1 ismade up by including the learning model generating device 10 and thetype identification device 20. The learning model generating device 10generates a first learning model and a second learning model both ofwhich will be described later. The type identification device 20 usesthe first learning model and the second learning model both generated bythe learning model generating device 10 to identify the type of an imagedefect contained on a print to be inspected.

The learning model generating device 10 includes a first control device11, an operation device 12, a display device 13, a storage device 14, acommunication interface device (communication I/F) 15, and a first imagereading device 16.

The operation device 12 is composed of a keyboard, a mouse, and so onand used to input commands and characters to the first control device 11and operate a pointer on the screen of the display device 13. Thedisplay device 13 is a display device, such as a liquid crystal display,and displays a response or a data result from the first control device11.

The storage device 14 is a storage device, such as an HDD (hard diskdrive), stores programs, including a first control program necessary forthe operation of the learning model generating device 10, and data, andincludes a learning model storage 141 for storing learning models.

The communication interface device 15 is an interface including anunshown communication module, such as a LAN (local area network) chip,and communicates with external devices. The learning model generatingdevice 10 transfers data to and from the type identification device 20via the communication interface device 15.

The first image reading device 16 includes a scanning mechanism (notshown) including a lighting part, a CCD (charge coupled device) sensor,and so on and optically reads, under the control of a first controller111 constituting part of the first control device 11, an image of adocument (a training print in this case) placed on an original glassplate (not shown). The reading of an image is performed at the maximumresolution (for example, 600 dpi) readable by the first image readingdevice 16.

For example, when the first image reading device 16 reads a trainingprint of A3 size (297 mm×210 mm) at a resolution of 600 dpi, an imageobtained by reading by the first image reading device 16 is composed of“9921×7016 pixels”. Examples of the training prints include printsconstituted of sets of prints P1 to P4 shown in FIG. 5.

The first control device 11 is made up by including a processor, a RAM(random access memory), a ROM (read only memory), and a dedicatedhardware circuit. The processor is, for example, a CPU (centralprocessing unit), an ASIC (application specific integrated circuit), anMPU (micro processing unit) or a GPU (graphic processing unit). Thefirst control device 11 includes the first controller 111, a firstsegmenter 112, a learning model generator 113, and a first compressor114.

The first control device 11 functions as the first controller 111, thefirst segmenter 112, the learning model generator 113, and the firstcompressor 114 by operation of the processor in accordance with a firstcontrol program stored in the storage device 14. However, each of theabove components, such as the first controller 111, may not beimplemented by the operation of the first control device 11 inaccordance with the first control program, but may be constituted by ahardware circuit. Hereinafter, the same applies to the other embodimentsunless otherwise stated.

The first controller 111 governs the overall operation control of thelearning model generating device 10. The first controller 111 isconnected to the operation device 12, the display device 13, the storagedevice 14, the communication interface device 15, and the first imagereading device 16, controls the operations of these connectedcomponents, and transfers signals or data to and from these components.

The first segmenter 112 segments, on a predetermined first segmentationcondition, each of the images of training prints obtained by readingperformed by the first image reading device 16. For example, if thefirst segmentation condition is set to segmentation of an image in unitsof “a×b pixels”, an image composed of “24a×24b pixels” is segmented into576 (=24×24) blocks of images as shown in FIG. 2A.

The learning model generator 113 learns the segmented images obtained bythe segmentation performed by the first segmenter 112 to generate afirst learning model for use in inferring the type of an image defectand allows the learning model storage 141 to store the generatedlearning model. For example, the learning model generator 113 learns, ina neural network, segmented images obtained by segmenting each of imagesof sets of prints P1 to P4 shown in FIG. 5 to generate a learning modelin which respective features of the segmented images are stored.

The learning model generator 113 learns a large number of images servingas a data set in the above manner and thus can generate a learning modelenabling the inference of an appropriate “output” from an unknown“input” as shown in FIG. 6. In FIG. 5, “color unevenness”, “streaking”,and “white spotting” are cited as types of image defects. However, thetypes of image defects are not limited to these types and other examplesinclude “scratch-like defect” and “black spotting”.

The first compressor 114 compresses, on a predetermined firstcompression condition, each of the images of the training printsobtained by reading performed by the first image reading device 16. Forexample, if the first compression condition is set at a compressionratio of 50%, an image composed of “24a×24b pixels” is compressed intoan image composed of “12a×12b pixels”.

As will be described later in details, the first segmenter 112 alsosegments each of compressed images of the training prints obtained bythe compression performed by the first compressor 114, and the learningmodel generator 113 also learns compressed and segmented images of thetraining prints obtained by the compression performed by the firstcompressor 114 and the subsequent segmentation performed by the firstsegmenter 112.

The type identification device 20 includes a second control device 21,an operation device 22, a display device 23, a storage device 24, acommunication interface device (communication I/F) 25, and a secondimage reading device 26.

The operation device 22 is composed of a keyboard, a mouse, and so onand used to input commands and characters to the second control device21 and operate a pointer on the screen of the display device 23. Thedisplay device 23 displays a response or a data result from the secondcontrol device 21.

The storage device 24 is a storage device, such as an HDD, storesprograms, including a second control program necessary for the operationof the type identification device 20, and data, and includes a learningmodel storage 241 for storing learning models.

The communication interface device 25 is an interface including anunshown communication module, such as a LAN chip, and communicates withexternal devices. The type identification device 20 transfers data toand from the learning model generating device 10 via the communicationinterface device 25.

The second image reading device 26 includes a scanning mechanism (notshown) including a lighting part, a CCD sensor, and so on and opticallyreads, under the control of a second controller 211 constituting part ofthe second control device 21, an image of a document (a print to beinspected in this case) placed on an original glass plate (not shown).The reading of an image is performed at the maximum resolution (forexample, 600 dpi) readable by the second image reading device 26. Theprint to be inspected is, for example, a print output from a printer, amultifunction peripheral or the like at a production site and is used toinspect the printer, the multifunction peripheral or the like.

The second control device 21 is made up by including a processor, a RAM,a ROM, and a dedicated hardware circuit. The processor is, for example,a CPU, an ASIC, an MPU or a GPU. The second control device 21 includesthe second controller 211, a second segmenter 212, an inferrer 213, anidentifier 214, and a second compressor 215.

The second control device 21 functions as the second controller 211, thesecond segmenter 212, the inferrer 213, the identifier 214, and thesecond compressor 215 by operation of the processor in accordance with asecond control program stored in the storage device 24. However, each ofthe above components, such as the second controller 211, may not beimplemented by the operation of the second control device 21 inaccordance with the second control program, but may be constituted by ahardware circuit. Hereinafter, the same applies to the other embodimentsunless otherwise stated.

The second controller 211 governs the overall operation control of thetype identification device 20. The second controller 211 is connected tothe operation device 22, the display device 23, the storage device 24,the communication interface device 25, and the second image readingdevice 26, controls the operations of these connected components, andtransfers signals or data to and from these components.

The second segmenter 212 segments, on the same segmentation condition asthe first segmentation condition (i.e., the segmentation condition usedby the learning model generating device 10 in generating the firstlearning model), the image of the print to be inspected obtained byreading performed by the second image reading device 26.

The inferrer 213 uses the first learning model to determine the type ofan image defect to which each of segmented images of the print obtainedby the segmentation performed by the second segmenter 212 applies,considers the determination result as an inference result, and outputsthe reliability of the inference result to the identifier 214. Forexample, the inferrer 213 outputs to the identifier 214 a quantifiedreliability of an inference result on the type of an image defect,indicating that the probability of “white spotting” is “99%”, theprobability of “streaking” is “0.5%”, and the probability of“scratch-like defect” is “0.5%”.

The identifier 214 determines, based on the inference results of theinferrer 213, whether or not any image defect occurs on the print to beinspected, and, on the occurrence of an image defect, identifies thetype of the image defect.

When determining that the reliabilities output from the inferrer 213include a reliability equal to or greater than a predetermined thresholdvalue (for example, 50%), the identifier 214 adopts the inference resultindicating the reliability to identify the type of the image defectcontained on the print to be inspected. On the other hand, whendetermining that the reliabilities output from the inferrer 213 includeno reliability equal to or greater than the above threshold value (inother words, when none of the segmented images shows a value equal to orgreater than the threshold value indicating abnormality), the identifier214 determines that the print to be inspected has no abnormality.

In relation to the reliability, it is conceivable that the inferenceresults regarding a print to be inspected suggest the possibility thatthere are different types of image defects represented by reliabilitiesequal to or greater than the threshold value and thus the exact type ofan image defect becomes difficult to identify. For example, it isconceivable that the inference result on the type of an image defect towhich a segmented image applies indicates that the probability of “whitespotting” is “56%”, while the inference result on the type of an imagedefect to which another segmented image applies indicates that theprobability of “streaking” is “58%”.

In such a case, the identifier 214 identifies, based on the inferenceresults of the inferrer 213, the type of an image defect contained onthe print to be inspected from the periodicity with which segmentedimages indicated as having an image defect appear on the print to beinspected.

For example, in the case where the image defect is “streaking”, thesegmented images indicated as having an image defect by inferenceresults appear with a constant period. In the case where the imagedefect is “white spotting”, the segmented images indicated as having animage defect by inference results are concentrated in an area.Therefore, it is possible to identify, from the periodicity with whichsegmented images indicated as having an image defect by inferenceresults appear on the print to be inspected, the type of an image defectcontained on the print to be inspected.

The second compressor 215 compresses, on the same compression conditionas the first compression condition (i.e., the compression condition usedby the learning model generating device 10 in generating the secondlearning model), the image of the print to be inspected obtained byreading performed by the second image reading device 26.

As will be described later in details, the second segmenter 212 alsosegments the compressed image of the print to be inspected obtained bythe compression performed by the second compressor 215, and the inferrer213 also infers the type of an image defect to which each of compressedand segmented images of the print to be inspected obtained by thecompression performed by the second compressor 215 and the subsequentsegmentation performed by the second segmenter 212 applies.

Next, a description will be given of an example of a sequence ofprocessing operations executed by the first control device 11 in thelearning model generating device 10, with reference to a flowchart shownin FIG. 3. This sequence of processing operations is a sequence ofprocessing operations to be performed when the first controller 111accepts, through the operation device 12, a user's instruction to readtraining prints.

First, the first controller 111 allows the first image reading device 16to read images of training prints (S1), the first segmenter 112segments, on the first segmentation condition, each of the images of thetraining prints obtained by the reading performed by the first imagereading device 16 (S2), and the learning model generator 113 learnssegmented images obtained by the segmentation performed by the firstsegmenter 112 (S3) to generate a first learning model for use ininferring a type of an image defect (S4).

As described previously, if the first segmentation condition is set tosegmentation of an image in units of “a×b pixels”, an image composed of“24a×24b pixels” subjected to the segmentation is segmented into 576(=24×24) blocks of images as shown in FIG. 2A. Such uncompressedsegmented images have small image regions as shown in FIG. 2A.Therefore, the first learning model generated by learning ofuncompressed segmented images is suitable for detection ofhigh-frequency abnormalities.

Subsequently, the first compressor 114 compresses, on the firstcompression condition, each of the images of the training printsobtained by the reading performed by the first image reading device 16(S5), the first segmenter 112 segments, on a predetermined secondsegmentation condition, each of compressed images of the training printsobtained by the compression performed by the first compressor 114 (S6),and the learning model generator 113 learns compressed and segmentedimages of the training prints obtained by the compression by the firstcompressor 114 and the subsequent segmentation by the first segmenter112 (S7) to generate a second learning model for use in inferring thetype of an image defect (S8).

For example, if the first compression condition is set at a compressionratio of 50%, an image composed of “24a×24b pixels” is compressed intoan image composed of “12a×12 b pixels”.

The second segmentation condition may be the same as the firstsegmentation condition. If the second segmentation condition is set tosegmentation of an image in units of “a×b pixels”, an image composed of“12a×12b pixels” subjected to the segmentation is segmented into 144(=12×12) blocks of images as shown in FIG. 2B. Such compressed andsegmented images have slightly larger image regions than theuncompressed segmented images, as shown in FIG. 2B. Therefore, thesecond learning model generated by learning of compressed and segmentedimages is suitable for detection of low-frequency abnormalities.

Subsequently, the first compressor 114 compresses, on a predeterminedsecond compression condition different from the first compressioncondition, each of the images of the training prints obtained by thereading performed by the first image reading device 16 (S9), the firstsegmenter 112 segments, on a predetermined third segmentation condition,each of compressed images of the training prints obtained by thecompression on the second compression condition performed by the firstcompressor 114 (S10), the learning model generator 113 learns compressedand segmented images of the training prints obtained by the compressionon the second compression condition by the first compressor 114 and thesubsequent segmentation by the first segmenter 112 (S11) to generate athird learning model for use in inferring the type of an image defect(S12), and then the first control device 11 ends this sequence ofprocessing operations.

For example, if the second compression condition is set at a compressionratio of 25%, an image composed of “24a×24b pixels” is compressed intoan image composed of “6a×6b pixels”.

The third segmentation condition may be the same as the firstsegmentation condition. If the third segmentation condition is set tosegmentation of an image in units of “a×b pixels”, an image composed of“6a×6b pixels” subjected to the segmentation is segmented into 36 (=6×6)blocks of images as shown in FIG. 2C. Such compressed and segmentedimages obtained at a large compression ratio have larger image regionsthan the compressed and segmented images obtained at a small compressionratio, as shown in FIG. 2C. Therefore, the third learning modelgenerated by learning of compressed and segmented images obtained at alarge compression ratio is more suitable for detection of low-frequencyabnormalities.

The description in S9 has been given of the case where the firstcompressor 114 compresses each of the images of the training prints onthe second compression condition different from the first compressioncondition. Instead of this, the first compressor 114 may furthercompress, on a predetermined third compression condition, each of thecompressed images obtained by the compression in S5. For example, thecompressed images obtained by compression at a compression ratio of 50%(i.e., on the first compression condition) in S5 may be furthercompressed at a compression ratio of 50% in S9. Subsequently, the aboveprocessing tasks in S10 to S12 are executed. Specifically, the firstsegmenter 112 segments, on the predetermined third segmentationcondition, each of compressed images of the training prints obtained bythe compression on the third compression condition performed by thefirst compressor 114 (S10), the learning model generator 113 learnscompressed and segmented images of the training prints obtained by thecompression on the third compression condition by the first compressor114 and the subsequent segmentation by the first segmenter 112 (S11) togenerate a third learning model for use in inferring the type of animage defect (S12), and then the first control device 11 ends thissequence of processing operations.

Next, a description will be given of an example of a sequence ofprocessing operations executed by the second control device 21 in thetype identification device 20, with reference to a flowchart shown inFIG. 4. This sequence of processing operations is a sequence ofprocessing operations to be performed when the second control device 211accepts, through the operation device 22, a user's instruction to read aprint to be inspected.

First, the second controller 211 allows the second image reading device26 to read an image of a print to be inspected (S21), the secondsegmenter 212 segments, on the first segmentation condition (used by thelearning model generating device 10 in generating the first learningmodel), the image of the print to be inspected obtained by the readingperformed by the second image reading device 26 (S22), and the inferrer213 uses the first learning model to infer the type of an image defectto which each of segmented images obtained by the segmentation performedby the second segmenter 212 applies, and outputs the reliabilities ofthe inference results to the identifier 214 (S23).

The identifier 214 determines whether or not there is any reliabilityequal to or greater than a predetermined threshold value T1 among thereliabilities input from the inferrer 213 (S24). When determining thatthere is any reliability equal to or greater than the threshold value T1(YES in S24), the identifier 214 identifies, based on the inferenceresults of the inferrer 213, the type of an image defect contained onthe print to be inspected from the periodicity with which segmentedimages indicated as having an image defect appear on the print to beinspected (S25), and then this sequence of processing operations isended.

By checking the above periodicity of appearance on the print to beinspected, the exact type of an image defect can be identified even ifthe inference results regarding the print to be inspected suggest thatthe print contains a plurality of types of image defects represented byreliabilities equal to or greater than the threshold value T1.Furthermore, even if the inference results show that the print to beinspected contains only one type of image defect represented byreliabilities equal to or greater than the threshold value T1, this doesnot necessarily result in identification of an origin (cause) of thedefect. For example, if there are “black spots” caused by a drum unit or“black spots” derived from some kind of dust on a print to be inspected,the origin of the image defect may not be able to be identified bylooking at an individual black spot, but can be identified by checkingthe periodicity of appearance of the black spots.

When, in S24, the identifier 214 determines that there is no reliabilityequal to or greater than the threshold value T1 among the reliabilitiesinput from the inferrer 213 (that is, none of the segmented imagesobtained by the segmentation in S22 shows a value equal to or greaterthan the threshold value T1 indicating abnormality) (NO in S24), thesecond compressor 215 compresses, on the first compression condition(used by the learning model generating device 10 in generating thesecond learning model), the image of the print to be inspected obtainedby the reading performed by the second image reading device 26 (S26).

Subsequently, the second segmenter 212 segments, on the secondsegmentation condition (used by the learning model generating device 10in generating the second learning model), a compressed image obtained bythe compression performed by the second compressor 215 (S27), and theinferrer 213 uses the second learning model to infer the type of animage defect to which each of compressed and segmented images obtainedby the compression performed by the second compressor 215 and thesubsequent segmentation performed by the second segmenter 212 applies,and outputs the reliabilities of the inference results to the identifier214 (S28).

The identifier 214 determines whether or not there is any reliabilityequal to or greater than a predetermined threshold value T2 among thereliabilities input from the inferrer 213 (S29). When determining thatthere is any reliability equal to or greater than the threshold value T2(YES in S29), the identifier 214 identifies, based on the inferenceresults of the inferrer 213, the type of an image defect contained onthe print to be inspected from the periodicity with which compressed andsegmented images indicated as having an image defect appear on the printto be inspected (S30), and then this sequence of processing operationsis ended.

By checking the above periodicity of appearance on the print to beinspected, the exact type of an image defect can be identified even ifthe inference results regarding the print to be inspected suggest thatthe print contains a plurality of types of image defects represented byreliabilities equal to or greater than the threshold value T2. Inaddition, the origin of an image defect which cannot be identified bylooking at an individual defective site can be identified by checkingthe periodicity of appearance of defective sites.

When, in S29, the identifier 214 determines that there is no reliabilityequal to or greater than the threshold value T2 among the reliabilitiesinput from the inferrer 213 (that is, none of the compressed andsegmented images obtained by the segmentation in S27 shows a value equalto or greater than the threshold value T2 indicating abnormality) (NO inS29), the second compressor 215 compresses, on the second compressioncondition (used by the learning model generating device 10 in generatingthe third learning model), the image of the print to be inspectedobtained by the reading performed by the second image reading device 26(S31).

Subsequently, the second segmenter 212 segments, on the thirdsegmentation condition (used by the learning model generating device 10in generating the third learning model), a compressed image obtained bythe compression performed by the second compressor 215 (S32), and theinferrer 213 uses the third learning model to infer the type of an imagedefect to which each of compressed and segmented images obtained by thecompression performed by the second compressor 215 and the subsequentsegmentation performed by the second segmenter 212 applies, and outputsthe reliabilities of the inference results to the identifier 214 (S33).

The identifier 214 determines whether or not there is any reliabilityequal to or greater than a predetermined threshold value T3 among thereliabilities input from the inferrer 213 (S34). When determining thatthere is any reliability equal to or greater than the threshold value T3(YES in S34), the identifier 214 identifies, based on the inferenceresults of the inferrer 213, the type of an image defect contained onthe print to be inspected from the periodicity with which compressed andsegmented images indicated as having an image defect appear on the printto be inspected (S35), and then this sequence of processing operationsis ended.

By checking the above periodicity of appearance on the print to beinspected, the exact type of an image defect can be identified even ifthe inference results regarding the print to be inspected suggest thatthe print contains a plurality of types of image defects represented byreliabilities equal to or greater than the threshold value T3. Inaddition, the origin of an image defect which cannot be identified bylooking at an individual defective site can be identified by checkingthe periodicity of appearance of defective sites.

When, in S34, the identifier 214 determines that there is no reliabilityequal to or greater than the threshold value T3 among the reliabilitiesinput from the inferrer 213 (that is, none of the compressed andsegmented images obtained by the segmentation in S32 shows a value equalto or greater than the threshold value T3 indicating abnormality) (NO inS34), the identifier 214 determines that the print to be inspected has“no abnormality”, i.e., no image defect, (S36), and then this sequenceof processing operations is ended.

The description in S31 is given of the case where the second compressor215 compresses the image of the print to be inspected on the secondcompression condition different from the first compression condition.Instead of this, the second compressor 215 may further compress, on apredetermined third compression condition, the compressed image obtainedby the compression in S26. For example, the compressed image obtained bycompression at a compression ratio of 50% (i.e., on the firstcompression condition) in S26 may be further compressed at a compressionratio of 50% in S31.

According to the above embodiment, the first learning model suitable fordetection of high-frequency abnormalities and the second and thirdlearning models suitable for detection of low-frequency abnormalitiesare generated. Therefore, both the high-frequency and low-frequencyabnormalities (image defects) can be appropriately detected. Inaddition, every image for use in generating the first to third learningmodels is in the form of segmented images. Therefore, even if the sizeof an image for use is large, the first to third learning models can begenerated without increasing the processing load.

In using a print output from a printer to inspect the printer, first, alarge amount of data set DS consisting of sets of prints P1 to P3 havingimage defects (for example, color unevenness, streaking, and whitespotting) and a set of prints P4 having no image defect is prepared asshown in FIG. 5, and learning of the large amount of data set DS isperformed in a neural network NN to generate a learning model M in whichfeatures of all the data are stored (a learning phase).

When such a large amount of data set DS including various types of datais learned in a neural network NN, a learning model M capable ofinferring an appropriate “output” from an unknown “input” can beobtained. For example, when, as shown in FIG. 6, the type of an imagedefect on an unknown print PA not found in the data set DS is inferredusing the learning model M, it can be determined that the print PA hasan image defect of “white spotting” (an inference phase).

Leaning in a neural network involves arithmetic processing of a hugeamount of data. Therefore, if the image size of a data set is too large,the data set is difficult to learn in a neural network.

In order to solve the above problem, it is necessary to reduce the imagesize of the data set. The following two methods for reducing the imagesize are considered. One of the methods is to subject the images of thedata set to compression processing and perform learning of compressedimages of the data set in a neural network. The other is to subject theimages of the data set to segmentation processing and perform learningof segmented images of the data set in a neural network.

However, if an image is excessively compressed, high-frequencyabnormalities (image defects), such as a small scratch-like defect, inthe image may not be able to be found. On the other hand, if an image issegmented into excessively small regions, high-frequency abnormalities,such as scratch-like defects, can be detected, but low-frequencyabnormalities, such as color unevenness, may not be able to be detectedas shown in FIGS. 7A and 7B. FIG. 7A shows a print PB on which an imagedefect of color unevenness forms, and FIG. 7B shows a state where theimage formed on the print PB is segmented and a block of the imagesegmented.

Therefore, if an image is subjected to compression processing orsegmentation processing, either high-frequency abnormalities orlow-frequency abnormalities may not be able to be detected. A techniquefor increasing the inspection accuracy is known, but a mechanism capableof detecting both of the above-mentioned high-frequency abnormalitiesand low-frequency abnormalities has not heretofore been developed.

In contrast, according to the above embodiment, even if the image sizeis large, both the high-frequency and low-frequency abnormalities (imagedefects) can be appropriately detected.

The description in the above embodiment has been given of the case whereby the learning of compressed and segmented images, two types oflearning models, i.e., the second learning model and the third learningmodel, are generated as learning models suitable for detection oflow-frequency abnormalities. However, the number of types ofabove-mentioned learning models generated is not limited to two andthree or more types of learning models may be generated. Needless tosay, a larger number of types of learning models provides a higheraccuracy in the identification of an image defect. Alternatively, it ispossible to generate the second learning model only as a learning modelfor detection of low-frequency abnormalities without generating thethird learning model, in which case the identification accuracydecreases.

Furthermore, the description in the above embodiment has been given ofthe case where the first learning model is first used to performinference and identification (S21 to S25 in FIG. 4), the second learningmodel is then subsequently used to perform inference and identification(S26 to S30), and the third learning model is then subsequently used toperform inference and identification (S31 to S35). However, in anotherembodiment, the inferrer 213 and the identifier 214 may perform theseinference and identification processing tasks in parallel and, uponsuccess of identification of the type of an image defect halfway, thesystem may end these processing tasks having been performed in paralleland proceed to next new processing. Thus, the processing time can bereduced.

Although the description in the above embodiment has been given of thecase where the learning model generating device 10 is formed as aseparate device from the type identification device 20, the typeidentification device 20 may have the above-described functions of thelearning model generating device 10.

The present disclosure is not limited to the above embodiment and can bemodified in various ways. Furthermore, the structure, configuration, andprocessing shown in the above embodiment with reference to FIGS. 1 to 7Bare merely illustrative of the present disclosure and not intended tolimit the present disclosure to the above particular structure,configuration, and processing.

While the present disclosure has been described in detail with referenceto the embodiments thereof, it would be apparent to those skilled in theart the various changes and modifications may be made therein within thescope defined by the appended claims.

What is claimed is:
 1. A learning model generating device capable oflearning a data set containing image defect-containing training printsprepared for each type of image defect to generate a learning model, thelearning model generating device comprising: a first image readingdevice that reads images of the training prints; and a first controldevice that includes a processor and functions, through the processorexecuting a first control program, as: a first segmenter that segments,on a predetermined first segmentation condition, each of the images ofthe training prints obtained by reading performed by the first imagereading device; a learning model generator that learns segmented imagesobtained by segmentation performed by the first segmenter to generate afirst learning model for use in inferring a type of an image defect; anda first compressor that compresses, on a predetermined first compressioncondition, each of the images of the training prints obtained by readingperformed by the first image reading device, wherein the first segmentersegments, on a predetermined second segmentation condition, each ofcompressed images obtained by compression performed by the firstcompressor, and the learning model generator learns compressed andsegmented images obtained by compression performed by the firstcompressor and segmentation performed by the first segmenter to generatea second learning model for use in inferring a type of an image defect.2. The learning model generating device according to claim 1, whereinthe first compressor compresses each of the images of the trainingprints on a predetermined second compression condition different fromthe first compression condition, the first segmenter segments, on apredetermined third segmentation condition, each of compressed images ofthe training prints obtained by compression on the second compressioncondition performed by the first compressor, and the learning modelgenerator learns compressed and segmented images obtained by compressionon the second compression condition performed by the first compressorand segmentation performed by the first segmenter to generate a thirdlearning model for use in inferring a type of an image defect.
 3. Thelearning model generating device according to claim 1, wherein the firstcompressor further compresses, on a predetermined third compressioncondition, each of the compressed images of the training prints obtainedby previous compression, the first segmenter segments, on apredetermined third segmentation condition, each of compressed images ofthe training prints obtained by further compression performed by thefirst compressor, and the learning model generator learns compressed andsegmented images obtained by the further compression performed by thefirst compressor and segmentation performed by the first segmenter togenerate a third learning model for use in inferring a type of an imagedefect.
 4. A type identification system comprising the learning modelgenerating device according to claim 1 and a type identification deviceusing the first learning model and the second learning model bothgenerated by the learning model generating device to identify a type ofan image defect contained on a print to be inspected, the typeidentification device comprising: a second image reading device thatreads an image of the print to be inspected; and a second control devicethat includes a processor and functions, through the processor executinga second control program, as: a second segmenter that segments, on thesame segmentation condition as the first segmentation condition, theimage of the print to be inspected obtained by reading performed by thesecond image reading device; an inferrer that uses the first learningmodel to infer a type of an image defect to which each of segmentedimages obtained by segmentation performed by the second segmenterapplies; and a second compressor that compresses, on the samecompression condition as the first compression condition, the image ofthe print to be inspected, wherein the second segmenter segments, on thesame segmentation condition as the second segmentation condition, acompressed image obtained by compression performed by the secondcompressor, and the inferrer uses the second learning model to infer atype of an image defect to which each of compressed and segmented imagesobtained by compression performed by the second compressor andsegmentation performed by the second segmenter applies.
 5. The typeidentification system according to claim 4, wherein on the learningmodel generating device the first compressor compresses each of theimages of the training prints on a predetermined second compressioncondition different from the first compression condition, the firstsegmenter segments, on a predetermined third segmentation condition,each of compressed images of the training prints obtained by compressionon the second compression condition performed by the first compressor,and the learning model generator learns compressed and segmented imagesobtained by compression on the second compression condition performed bythe first compressor and segmentation performed by the first segmenterto generate a third learning model for use in inferring a type of animage defect, and wherein on the type identification device the secondcompressor compresses, on the same compression condition as the secondcompression condition, the image of the print to be inspected, thesecond segmenter segments, on the same segmentation condition as thethird segmentation condition, a compressed image obtained by compressionon the second compression condition performed by the second compressor,and the inferrer uses the third learning model to infer a type of animage defect to which each of compressed and segmented images obtainedby compression on the second compression condition performed by thesecond compressor and segmentation performed by the second segmenterapplies.
 6. The type identification system according to claim 4, whereinon the learning model generating device the first compressor furthercompresses, on a predetermined third compression condition, each of thecompressed images of the training prints obtained by previouscompression, the first segmenter segments, on a predetermined thirdsegmentation condition, each of compressed images of the training printsobtained by further compression performed by the first compressor, andthe learning model generator learns compressed and segmented imagesobtained by the further compression performed by the first compressorand segmentation performed by the first segmenter to generate a thirdlearning model for use in inferring a type of an image defect, andwherein on the type identification device the second compressor furthercompresses, on the same compression condition as the third compressioncondition, the compressed image of the print to be inspected obtained byprevious compression, the second segmenter segments, on the samesegmentation condition as the third segmentation condition, a compressedimage obtained by further compression performed by the secondcompressor, and the inferrer uses the third learning model to infer atype of an image defect to which each of compressed and segmented imagesobtained by the further compression performed by the second compressorand segmentation performed by the second segmenter applies.
 7. The typeidentification system according to claim 4, wherein on the typeidentification device the second control device further functions,through the processor executing the second control program, as anidentifier that determines, based on inference results of the inferrer,whether or not any image defect occurs on the print to be inspected,and, on occurrence of an image defect, identifies a type of the imagedefect, when using the first learning model to infer a type of an imagedefect to which each of the segmented images obtained by segmentationperformed by the second segmenter applies, the inferrer outputsrespective reliabilities of the inference results using the firstlearning model to the identifier, when using the second learning modelto infer a type of an image defect to which each of the compressed andsegmented images obtained by compression performed by the secondcompressor and segmentation performed by the second segmenter applies,the inferrer outputs respective reliabilities of the inference resultsusing the second learning model to the identifier, (i) when determiningthat the reliabilities output from the inferrer include a reliabilityequal to or greater than a predetermined threshold value, the identifieradopts the inference result indicating the reliability to identify atype of an image defect contained on the print to be inspected, and (ii)when determining that the reliability output from the inferrer includeno reliability equal to or greater than the threshold value, theidentifier determines that the print to be inspected has no abnormality.8. The type identification system according to claim 4, wherein on thetype identification device the second control device further functions,through the processor executing the second control program, as anidentifier that identifies, based on inference results of the inferrer,a type of an image defect contained on the print to be inspected fromperiodicity with which the segmented images indicated as having an imagedefect appear on the print to be inspected.
 9. A method for generating alearning model by learning a data set containing image defect-containingtraining prints prepared for each type of image defect, the methodcomprising: an image reading step of reading images of the trainingprints; a segmentation step of segmenting, on a predetermined firstsegmentation condition, each of the images of the training printsobtained by reading in the image reading step; a learning modelgenerating step of learning segmented images obtained by segmentation inthe segmentation step to generate a first learning model for use ininferring a type of an image defect; and a compression step ofcompressing each of the images of the training prints on a predeterminedfirst compression condition, wherein in the segmentation step each ofcompressed images obtained by compression in the compression step issegmented on a predetermined second segmentation condition, and in thelearning model generating step, compressed and segmented images obtainedby compression in the compression step and segmentation in thesegmentation step are learned to generate a second learning model foruse in inferring a type of an image defect.